1. Field of the Invention
The present invention relates to providing the capability for peer processes in an application server cluster to detect failure of and recover transactions from any application server in the cluster.
2. Description of the Related Art
Statistical data mining techniques help to reveal subtle structures, construct concepts, and evaluate trends that lie hidden within data. With the availability of huge amounts fine of grain data, granularization of information of different grain size (information chunks) has become a necessity in order to understand and assimilate the nature of the data in a more meaningful and human-like fashion. The subjective information like ‘young-age’, ‘low-cholesterol’, ‘expensive homes’ etc. can be very easily modeled by fuzzy sets, through linguistic variables. This linguistic information can easily capture the inherent uncertainty in a more human-like fashion and as a result are able to describe and effectively use imprecise information.
Traditional classification techniques in pattern recognition are based on probability models that are generated using large amounts of training data. In that framework, the Bayes classifier is the simplest classifier, and yet it performs surprisingly well for many practical domains like text classification, medical diagnosis, etc. However, even the simple Bayesian classifier is based upon an unrealistic assumption that the features are independent in a given class. In other words,
            P      ⁡              (                  x          ⁢                      |                    ⁢                      C            j                          )              =                  ∏                  i          =          1                n            ⁢              P        ⁡                  (                                    x              i                        ⁢                          ❘                        ⁢                          C              j                                )                      ,
where x={xi, . . . , xn}, and Cj is the jth class. The success of Bayes classifier in the presence of feature dependencies may be explained as an optimality criterion under a zero-one loss function. The detection of attribute dependence is not necessarily the best approach to understand or even evaluate the Bayes classifier. Any attempt to model the joint distribution of the training data having independent distribution functions, such as Gaussian etc., may not be the right approach to model the data.
Thus, a need arises for a technique involving a classifier that provides better understanding and an improved approach to modeling the data.